Multi-Modal Image Fusion for Medical Diagnosis: Combining MRI And CT Using Deep Generative Models

Medical imaging plays a pivotal role in early disease detection, surgical planning, and post-treatment monitoring. Among various imaging modalities, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are widely used due to their complementary strengths MRI offers superior soft tissue contrast, while CT excels in visualizing dense structures like bones. However, relying on a single modality often limits diagnostic precision, especially in complex clinical cases such as brain tumors, stroke evaluation, and orthopedic planning. Consequently, fusing information from both MRI and CT images into a unified representation, known as multi-modal image fusion, has become an essential approach to enhance diagnostic accuracy and clinical decision-making. Traditional image fusion techniques, including wavelet transforms, PCA, and Laplacian pyramids, have demonstrated limited performance due to their reliance on handcrafted features and linear transformations. These methods often result in loss of critical structural or functional information. The rise of deep learning, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), has revolutionized image processing by enabling the automatic extraction and synthesis of complex features across domains. These generative models not only learn hierarchical representations from data but also preserve the contextual integrity of both modalities during the fusion process. GANs have shown exceptional capability in generating realistic, high-resolution images, while VAEs offer probabilistic latent space modeling that is beneficial for uncertainty estimation in medical contexts. This study proposes and evaluates a multi-modal image fusion framework leveraging GAN and VAE architectures to combine MRI and CT images for enhanced medical diagnosis. The research utilizes multiple publicly available datasets (e.g., TCIA, BraTS), implements both pixel-level and feature-level fusion strategies, and conducts extensive evaluations using quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and classification accuracy for downstream diagnostic tasks. The experimental results demonstrate that the proposed GAN- and VAE-based fusion models significantly outperform traditional fusion techniques in terms of visual clarity, preservation of diagnostic details, and computational efficiency. Moreover, a comprehensive benchmark of different model variants (Pix2Pix, CycleGAN, β-VAE, CVAE-GAN) is provided to guide future research and clinical implementation. This work contributes to the field by offering a reproducible framework, open-source implementation, and actionable insights for integrating deep generative models into radiological workflows, thereby paving the way for AI-augmented precision diagnostics.